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Polymers, a macromolecule within materials science and engineering, play a pervasive role in daily life. They offer adaptability and are customisable to acquire specific and desirable properties, such as flexibility, water resistance, or electrical conductivity.
This versatility finds application in an array of products we encounter, from the nonstick cookware that simplifies our culinary adventures to the construction materials that shape our built environment, where polymers like polytetrafluoroethylene and polyvinyl chloride prominently feature.
However, polymers have their challenges, particularly when identifying the ideal combinations of materials to engineer the most effective and tailored polymers. The scope for potential material combinations is virtually boundless.
Fortunately, the landscape is evolving with the advent of cutting-edge technology. By adopting machine learning, this development has the potential to utterly transform how scientists and manufacturers navigate the sprawling chemical space, allowing them to pinpoint and craft these pivotal polymers with greater precision and efficiency than ever before.
Engineer Rampi Ramprasad conceived and directed the project. The primary goal of the new tool is to address the challenges associated with exploring the vast chemical space of polymers. PolyBERT, its name, has undergone extensive training using a comprehensive dataset containing 80 million polymer chemical structures. Consequently, it has become proficient in deciphering the intricate language of polymers.
Ramprasad stated, “This represents a pioneering application of language models in the field of polymer informatics. While natural language models are commonly employed to extract materials data from literature, our objective here is to apply such capabilities to comprehend the intricate grammar and syntax governing the assembly of atoms in polymer formation.”
PolyBERT approaches chemical structures and atomic connections as a specialised form of chemical communication, utilising cutting-edge techniques inspired by natural language processing to glean the most significant insights from these structures. Employing a Transformer architecture like that found in natural language models, it excels at capturing intricate patterns and relationships while mastering the grammar and syntax that govern atomic arrangements within polymer structures and beyond.
An impressive attribute of PolyBERT is its speed. Compared to traditional methodologies, PolyBERT outpaces them by more than two orders of magnitude in processing velocity. This rapid processing capacity positions PolyBERT as an invaluable tool within high-throughput polymer informatics pipelines. It enables swift and efficient screening of extensive polymer landscapes, offering researchers a powerful means to explore and analyse vast datasets quickly and effectively. This newfound speed and efficiency hold the potential to significantly accelerate advancements in the field of polymer research and development.
The researchers anticipate that the computation time required for polyBERT fingerprints will see further enhancements due to advancements in graphics processing unit (GPU) technology.
Debora Rodrigues, a programme director within NSF’s Directorate for Technology, Innovation, and Partnerships, explained that researchers are developing a novel artificial intelligence tool. This tool is designed to address the challenge of identifying the most effective polymer combinations, and it utilises artificial intelligence to achieve this goal. The device is trained on an extensive 80 million polymer chemical structures dataset. This approach enables the swift screening of diverse polymers without requiring labour-intensive laboratory experiments.
